5G promises new revenue enabled by an ultra-reliable, low latency (URLLC) network powered by a highly distributed services-based architecture and edge computing (MEC). The transport network underpins them all. End-to-end visibility is required to detect latency issues in real time, before they impact customer experience and business-critical services.
Demand for real-time services like HD video streaming, remote working, and online gaming is pushing service providers to reduce latency. New value-added services (e.g., network slicing and URLLC) demand minimal, predictable latency. Isolating the latency contribution of dynamic infrastructure in multi-domain, multi-layer networks requires a new approach.
Variations in QoS across dynamic infrastructure directly impact user experience. Low customer NPS is correlated with higher churn and higher marketing expenses. Pinning down the contribution of virtualized and cloud-native network functions to QoE is particularly challenging when the network topology is constantly changing.
Cloud-native 5G networks depend on ephemeral resources that scale as network usage ebbs and flows. This complicates efforts to achieve a holistic picture of the network and keep it current. A lack of end-to-end visibility makes it difficult to determine the sources and causes of latency and it complicates efforts to understand, improve and optimize the contributors to QoE.
Service providers are counting on new SLA-backed capabilities to generate the revenues required to fund network investments. Drones, autonomous guided vehicles (AGVs) and even virtual reality applications require consistent, ultra-low levels of latency to perform their best.
Optimizing the transport network in this more dynamic, complex, and business-critical context requires an inclusive, end-to-end assurance strategy. All groups—RAN, core, transport network, traffic engineering, network planning, cloud infrastructure—must do their part to shave precious millisecond delays out of each domain, while also identifying and resolving bottlenecks where they intersect.
Understanding the latency contribution of each network element is critical to optimizing the transport network
With cloud-native test agents, detailed topology, source-agnostic data ingestion and multi-domain, multi-layer analytics, EXFO’s adaptive service assurance platform provides full visibility and diagnostics into complex transport networks, and the services and user experiences they support.
Synthetic test agents deployed along the service path provide segment--by--segment performance and latency metrics in dynamic networks. Real-time network topology helps to pinpoint the sources of problematic latency, enabling rapid and effective action.
Full network visibility, from radio to core, across dynamic xhaul, metro and long-haul transport networks in context of service and network topology, showing utilization and the underlying fiber infrastructure.
Streaming analytics reveal anomalies, bottlenecks and their root causes, and help to prescribe actionable steps to permanently resolve issues and systematically optimize latency.
EXFO offers the most complete range of granular active monitoring tests and coverage available for measuring and evaluating customer QoE. Employing cloud-native virtualized and physical form factors, they are easily deployed, scaled and orchestrated across complex multi-domain networks and cloud infrastructure.